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Entry-level Data Science professional focused on Big Data & Machine Learning.
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Highly enthusiastic and self-motivated Big Data developer aspiring to solve real-world problems using technology. Graduated with a Bachelor of Technology in Computer Science and currently exploring Big Data, Data Engineering, and Data Science. Passionate about new tools and technologies, always eager to learn and grow as a professional.
Malabar Institute of Technology
Bachelor Of Technology · Computer Science & Engineering
August 1, 2017 – June 30, 2021
Safa Ems School
12th · Computer Science (CBSE)
June 1, 2015 – May 31, 2017
Safa Ems School
10th · Computer Science (CBSE)
June 1, 2014 – May 31, 2015
HR Analytics Dashboard (Power BI)
June 18, 2026 – Present
Developed an interactive HR Analytics Dashboard in Power BI to analyze employee attrition and workforce trends. Performed data cleaning and transformation using Power Query, including handling null values, duplicate records, and data type corrections. Created DAX measures and calculated columns to compute KPIs such as Attrition Rate, Average Salary, Average Age, and Employee Count. Built interactive visualizations including KPI cards, bar charts, donut charts, slicers, and matrix tables for data-driven insights. Analyzed employee attrition across age groups, salary slabs, departments, education fields, and job roles. Identified key factors contributing to employee turnover, helping support retention and workforce planning decisions. Designed a user-friendly dashboard with dynamic filtering and drill-down capabilities for enhanced analysis.
Gold Price Prediction Using Machine Learning
June 18, 2026 – Present
Developed a Machine Learning model to predict gold prices using financial indicators such as S&P 500 Index (SPX), Oil Prices (USO), Silver Prices (SLV), and EUR/USD exchange rates. Performed data preprocessing, exploratory data analysis (EDA), correlation analysis, and feature selection on a dataset containing 2,290 records. Implemented a Random Forest Regressor using Scikit-learn for gold price prediction. Visualized data distributions and feature correlations using Matplotlib and Seaborn to identify key factors influencing gold prices. Applied train-test split methodology (80:20) and evaluated model performance using the R2 Score metric. Achieved approximately 98% prediction accuracy (R² Score ≈ 0.98) on the test dataset. Built an end-to-end machine learning pipeline including data collection, preprocessing, model training, prediction, and evaluation.
Data Science
Nucot
June 1, 2026 – Present
Big Data Analytics
Zeyobron Analytics
June 1, 2026 – Present
Java full Stack development
J-Spiders
June 1, 2026 – Present
Data Analytics Intership
Zaalima Development
June 1, 2026 – Present
Azure Cloud Fundamentals
Magic bus
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate an interest in both predictive analytics (Gold Price Prediction) and business intelligence/HR analytics (HR Analytics Dashboard), indicating a diverse application of data science skills. The listed certifications in Data Science, Big Data Analytics, and Azure Cloud Fundamentals suggest a proactive approach to skill development and alignment with modern data ecosystems. The target role of 'Data Science' aligns well with the candidate's project experience and stated technical skills. However, the lack of professional experience limits the assessment of cultural fit in a team or corporate environment.
Soft Skills & Operational Fit
The candidate's professional summary indicates enthusiasm, self-motivation, and eagerness to learn new tools and technologies, which are positive indicators for operational fit and continuous improvement. However, without specific behavioral assessment data, a comprehensive evaluation of soft skills like teamwork, problem-solving under pressure, or communication in a collaborative setting is not possible.